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Update app.py
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app.py
CHANGED
@@ -44,7 +44,7 @@ for kn in np.linspace(0.2, 2, 100):
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def plot_curve(kn, kd):
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fig = plt.figure()
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plt.plot(kns, overheads, color="black")
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plt.scatter([kn], [compute_overhead(kn, kd)*100], marker="
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plt.xlabel("Fraction of compute optimal model size")
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plt.ylabel("Compute overhead (%)")
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plt.legend(loc="best")
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@@ -70,10 +70,12 @@ Compute budget (TFLOPs): {C:.2E}
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## Chinchilla optimal:
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Optimal model size:\t\t {N_opt/Bn:.2f}B
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Optimal datset size (tokens):\t {D_opt/Bn:.2f}
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## Your setting trade-off:
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Training compute overhead (%):\t {100*compute_overhead(kn, kd):.2f}
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Inference cost fraction (%):\t {kn*100:.2f}"""
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return text, fig
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def plot_curve(kn, kd):
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fig = plt.figure()
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plt.plot(kns, overheads, color="black")
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plt.scatter([kn], [compute_overhead(kn, kd)*100], marker="x", c="red", label="You are here!")
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plt.xlabel("Fraction of compute optimal model size")
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plt.ylabel("Compute overhead (%)")
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plt.legend(loc="best")
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## Chinchilla optimal:
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Optimal model size:\t\t {N_opt/Bn:.2f}B
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Optimal datset size (tokens):\t {D_opt/Bn:.2f}
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## Your setting trade-off:
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Training compute overhead (%):\t {100*compute_overhead(kn, kd):.2f}
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Inference cost fraction (%):\t {kn*100:.2f}"""
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return text, fig
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